Use "take the best": choose on your single most informative cue
When choosing between options, identify the most diagnostic cue and use it — stop searching for more.
Why it works
Take the Best is a lexicographic heuristic: rank your cues by validity, check the best one first, and make your decision if it discriminates. Only consult further cues if the top one doesn’t discriminate. This ignores a great deal of information — and in uncertain real-world environments, it often outperforms regression models that use all the information, because each additional cue adds noise alongside signal.
How to do it
- For a recurring decision, write down the three most predictive cues you’ve observed historically.
- Order them by how often they’ve been right when they pointed in a direction.
- When facing the decision, check the top cue first. If it clearly points one way, stop — act on it without consulting the others.
Evidence
Gigerenzer and colleagues showed that Take the Best outperformed multiple regression on real datasets in out-of-sample prediction across several domains, particularly when data was limited and the environment was uncertain. (observational)
Take the Best outperforms regression under specific conditions — noisy environment, limited data, correlated cues. In large, clean datasets with many valid independent predictors, regression typically wins.
Sources
- Gigerenzer & Goldstein (1996), Reasoning the fast and frugal way, Psychological Review
Common mistake
Using Take the Best in data-rich, stable environments where all cues carry independent signal — here, looking at more cues genuinely helps, and stopping at one is costly.
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More practices for Simple Heuristics: Gerd Gigerenzer’s Case for Fast and Frugal Thinking
- Trust the recognition heuristic in uncertain environments
If you recognize one option and not the other, the recognized one is usually better — in the right domain.
- Satisfice: set a good-enough threshold and stop searching when you hit it
Optimize for "good enough" rather than "best possible" — the search cost often exceeds the gain.
- Set default rules for willpower-intense situations
A pre-decided rule requires no willpower at the decision point — the decision has already been made.
- Match your heuristic to the structure of the environment
A good rule works because it matches the statistical regularities of the environment — wrong environment, wrong rule.
- Use the 1/N rule for diversification under deep uncertainty
When you cannot estimate the value of each option reliably, spread resources equally.
- Build your personal adaptive toolbox of domain-specific rules
The goal isn’t one universal heuristic — it’s a curated collection that matches the domains you actually navigate.